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test_shadow_glossy.py
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import pyredner
import numpy as np
import torch
# Optimize four vertices of a shadow blocker, where the receiver is highly glossy
# Use GPU if available
pyredner.set_use_gpu(torch.cuda.is_available())
# Set up the scene using Pytorch tensor
position = torch.tensor([0.0, 2.0, -4.0])
look_at = torch.tensor([0.0, -2.0, 0.0])
up = torch.tensor([0.0, 1.0, 0.0])
fov = torch.tensor([45.0])
clip_near = 1e-2
resolution = (256, 256)
cam = pyredner.Camera(position = position,
look_at = look_at,
up = up,
fov = fov,
clip_near = clip_near,
resolution = resolution)
mat_shiny = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0], device = pyredner.get_device()),
specular_reflectance = torch.tensor([1.0, 1.0, 1.0], device = pyredner.get_device()),
roughness = torch.tensor([0.0005], device = pyredner.get_device()))
mat_grey = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.5, 0.5, 0.5],
device = pyredner.get_device()))
mat_black = pyredner.Material(\
diffuse_reflectance = torch.tensor([0.0, 0.0, 0.0],
device = pyredner.get_device()))
materials = [mat_shiny, mat_grey, mat_black]
floor_vertices = torch.tensor([[-4.0,0.0,-4.0],[-4.0,0.0,4.0],[4.0,0.0,-4.0],[4.0,0.0,4.0]],
device = pyredner.get_device())
floor_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
shape_floor = pyredner.Shape(floor_vertices, floor_indices, None, None, 0)
blocker_vertices = torch.tensor(\
[[0.0,1.0,0.5],[0.0,3.0,0.5],[0.8,1.0,0.5],[0.8,3.0,0.5]],
device = pyredner.get_device())
blocker_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
shape_blocker = pyredner.Shape(blocker_vertices, blocker_indices, None, None, 1)
light_vertices = torch.tensor(\
[[-2.0,0.0,4.0],[-2.0,12.0,4.0],[2.0,0.0,4.0],[2.0,12.0,4.0]],
device = pyredner.get_device())
light_indices = torch.tensor([[0,1,2], [1,3,2]],
device = pyredner.get_device(), dtype = torch.int32)
shape_light = pyredner.Shape(light_vertices, light_indices, None, None, 2)
shapes = [shape_floor, shape_blocker, shape_light]
light_intensity = torch.tensor([0.5, 0.5, 0.5])
# The first argument is the shape id of the light
light = pyredner.AreaLight(2, light_intensity)
area_lights = [light]
scene = pyredner.Scene(cam, shapes, materials, area_lights)
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 1)
# Alias of the render function
render = pyredner.RenderFunction.apply
# Render our target
img = render(0, *args)
pyredner.imwrite(img.cpu(), 'results/test_shadow_glossy/target.exr')
pyredner.imwrite(img.cpu(), 'results/test_shadow_glossy/target.png')
target = pyredner.imread('results/test_shadow_glossy/target.exr')
if pyredner.get_use_gpu():
target = target.cuda()
# Perturb the scene, this is our initial guess
shape_blocker.vertices = torch.tensor(\
[[-0.6,0.9,0.4],[-0.8,3.3,0.7],[0.2,1.1,0.6],[0.3,3.2,0.4]],
device = pyredner.get_device(),
requires_grad=True)
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 1)
# Render the initial guess
img = render(1, *args)
pyredner.imwrite(img.cpu(), 'results/test_shadow_glossy/init.png')
diff = torch.abs(target - img)
pyredner.imwrite(diff.cpu(), 'results/test_shadow_glossy/init_diff.png')
# Optimize for blocker vertices
optimizer = torch.optim.Adam([shape_blocker.vertices], lr=5e-2)
for t in range(200):
print('iteration:', t)
optimizer.zero_grad()
# Forward pass: render the image
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 4,
max_bounces = 1)
img = render(t+1, *args)
pyredner.imwrite(img.cpu(), 'results/test_shadow_glossy/iter_{}.png'.format(t))
loss = (img - target).pow(2).sum()
print('loss:', loss.item())
loss.backward()
print('grad:', shape_blocker.vertices.grad)
optimizer.step()
print('vertices:', shape_blocker.vertices)
args = pyredner.RenderFunction.serialize_scene(\
scene = scene,
num_samples = 1024,
max_bounces = 1)
img = render(202, *args)
pyredner.imwrite(img.cpu(), 'results/test_shadow_glossy/final.exr')
pyredner.imwrite(img.cpu(), 'results/test_shadow_glossy/final.png')
pyredner.imwrite(torch.abs(target - img).cpu(), 'results/test_shadow_glossy/final_diff.png')
from subprocess import call
call(["ffmpeg", "-framerate", "24", "-i",
"results/test_shadow_glossy/iter_%d.png", "-vb", "20M",
"results/test_shadow_glossy/out.mp4"])